Effect of Addition of Treated Coir Fibres on the Compression Behaviour of Clay

This paper presents the effect of treated coir fibres (15 mm in length) on the unconfined compressive strength of clay. Dry, sodium hydroxide and carbon tetrachloride–treated coir fibres were used in the study. The coir fibre content was varied from 0.4% to 1.6%. The results indicated that the unconfined compressive strength of clay and clay with dry coir fibres can be increased by treatment with carbon tetrachloride and sodium hydroxide. The increase in unconfined compressive strength was highest with carbon tetrachloride treatment. The clay reinforced with treated fibres was able to bear higher strains at failure as compared to clay and clay with dry fibres. With the increase in coir fibre content (0.4%-1.6%) in clay, there was an increase in the unconfined compressive strength. The clay with treated coir fibres can be used for making bricks for mud houses in rural India

Finite-Element Limit Analysis of Strip and Circular Skirted Footings on Sand

The lower- and upper-bound theorems of the limit analysis have been used in conjunction with finite elements and second-order cone programming (SOCP) for determining the bearing capacity of strip and circular skirted footings on sand. The analysis follows the Mohr-Coulomb's yield criterion and the associated flow rule; sand is not usually considered to obey this rule, but the results of using it are discussed. The friction angle of sand was varied between 30 and 45 degrees, and the depth (D-s) of the skirt increased from 0.25 to 2B; here B implies: (1) the width of a skirted strip footing, and (2) the diameter of a circular skirted footing. The results are expressed in terms of the bearing capacity ratio (BCR): the ratio of the bearing capacities of a skirted footing to that of the surface footing, with the same value of B but without any skirt element. The results reveal that the magnitude of the BCR increases quite extensively with an increase in the value of D-s/B. The skirted footing was found to be especially quite advantageous for loose sand. With the same D-s/B, the BCR for a circular skirted footing was found to be substantially greater than that for the strip skirted footing

Bearing capacity and settlement prediction of multi-edge skirted footings resting on sand

Este documento presenta la aplicación de redes neuronales artificiales (ANN) y el análisis de regresión multivariable (MRA) para predecir la capacidad de carga y el asentamiento de las zapatas bordeadas de bordes múltiples en arena. Estos parámetros se definen, respectivamente, en términos de la relación de capacidad de carga (BCR) de carga de la zapata con zócalo y sin zócalo y el factor de reducción de asentamiento (SRF), la razón de la diferencia en la solución de zócalo sin zócalo y zapatas bordeadas para el asentamiento de zapatas sin falda a una presión determinada. Las ecuaciones modelo para predecir la BCR y el SRF de la zapata de forma regular se desarrollaron primero utilizando los datos disponibles recopilados de la literatura. Estas ecuaciones se modificaron posteriormente para predecir la BCR y el SRF de la zapata bordeada de bordes multiples, para la cual se generaron los datos mediante la realización de una prueba de laboratorio a pequeña escala. Los parámetros de entrada elegidos para desarrollar modelos ANN fueron el ángulo de fricción interna (ϕ), la profundidad del faldón (Ds) al ancho de la relación de zapata (B) para la predicción del BCR; en cuanto al SRF, se consideró un parámetro de entrada adicional: la tensión normal (?). La arquitectura para los modelos ANN desarrollados fue 2-2-1 y 3-2-1 para la BCR y el SRF, respectivamente. El R2 para las zapatas bordeadas de bordes múltiples estuvo en el rango de 0,940-0,977 para el modelo ANN y 0,827-0,934 para el análisis de regresión. De manera similar, el R2 para la predicción del SRF pudo haber sido de 0,913-0,985 para el modelo ANN y 0,739-0,932 para el análisis de regresión. Se reveló que la BCR predicha y el SRF para las zapatas con borde de múltiples bordes con el uso de ANN es superior al MRA. Además, los resultados del análisis de sensibilidad indican que tanto el BCR como el SRF de las zapatas bordeadas de bordes múltiples se ven más afectados por la profundidad de la falda, seguida del ángulo de fricción de la arena.This paper presents the application of artificial neural networks (ANN) and multivariable regression analysis (MRA) to predict the bearing capacity and the settlement of multi-edge skirted footings on sand. Respectively, these parameters are defined in terms of the bearing capacity ratio (BCR) of skirted to unskirted footing and the settlement reduction factor (SRF), the ratio of the difference in settlement of unskirted and skirted footing to the settlement of unskirted footing at a given pressure. The model equations for the prediction of the BCR and the SRF of the regular shaped footing were first developed using the available data collected from the literature. These equations were later modified to predict the BCR and the SRF of the multi-edge skirted footing, for which the data were generated by conducting a small scale laboratory test. The input parameters chosen to develop ANN models were the angle of internal friction (ϕ) and skirt depth (Ds) to the width of the footing (B) ratio for the prediction of the BCR; as for the SRF one additional input parameter was considered: normal stress (?). The architecture for the developed ANN models was 2-2-1 and 3-2-1 for the BCR and the SRF, respectively. The R2 for the multi-edge skirted footings was in the range of 0,940-0,977 for the ANN model and 0,827-0,934 for the regression analysis. Similarly, the R2 for the SRF prediction might have been 0,913-0,985 for the ANN model and 0,739-0,932 for the regression analysis. It was revealed that the predicted BCR and SRF for the multi-edge skirted footings with the use of ANN is superior to MRA. Furthermore, the results of the sensitivity analysis indicate that both the BCR and the SRF of the multi-edge skirted footings are mostly affected by skirt depth, followed by the friction angle of the sand